A Choropleth Map is a map composed of colored polygons. It is used to represent spatial variations of a quantity. This page documents how to build tile-map choropleth maps, but you can also build outline choropleth maps.
Below we show how to create Choropleth Maps using either Plotly Express' px.choropleth_map function or the lower-level go.Choroplethmap graph object.
Making choropleth maps requires two main types of input:
id field or some identifying value in properties.The GeoJSON data is passed to the geojson argument, and the data is passed into the color argument of px.choropleth_map (z if using graph_objects), in the same order as the IDs are passed into the location argument.
Note the geojson attribute can also be the URL to a GeoJSON file, which can speed up map rendering in certain cases.
feature.id¶Here we load a GeoJSON file containing the geometry information for US counties, where feature.id is a FIPS code.
from urllib.request import urlopen
import json
with urlopen('https://raw.githubusercontent.com/plotly/datasets/master/geojson-counties-fips.json') as response:
counties = json.load(response)
counties["features"][0]
import pandas as pd
df = pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/fips-unemp-16.csv",
dtype={"fips": str})
df.head()
Plotly Express is the easy-to-use, high-level interface to Plotly, which operates on a variety of types of data and produces easy-to-style figures.
With px.choropleth_map, each row of the DataFrame is represented as a region of the choropleth.
from urllib.request import urlopen
import json
with urlopen('https://raw.githubusercontent.com/plotly/datasets/master/geojson-counties-fips.json') as response:
counties = json.load(response)
import pandas as pd
df = pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/fips-unemp-16.csv",
dtype={"fips": str})
import plotly.express as px
fig = px.choropleth_map(df, geojson=counties, locations='fips', color='unemp',
color_continuous_scale="Viridis",
range_color=(0, 12),
map_style="carto-positron",
zoom=3, center = {"lat": 37.0902, "lon": -95.7129},
opacity=0.5,
labels={'unemp':'unemployment rate'}
)
fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
fig.show()
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If the GeoJSON you are using either does not have an id field or you wish you use one of the keys in the properties field, you may use the featureidkey parameter to specify where to match the values of locations.
In the following GeoJSON object/data-file pairing, the values of properties.district match the values of the district column:
import plotly.express as px
df = px.data.election()
geojson = px.data.election_geojson()
print(df["district"][2])
print(geojson["features"][0]["properties"])
To use them together, we set locations to district and featureidkey to "properties.district". The color is set to the number of votes by the candidate named Bergeron.
import plotly.express as px
df = px.data.election()
geojson = px.data.election_geojson()
fig = px.choropleth_map(df, geojson=geojson, color="Bergeron",
locations="district", featureidkey="properties.district",
center={"lat": 45.5517, "lon": -73.7073},
map_style="carto-positron", zoom=9)
fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
fig.show()
In addition to continuous colors, we can discretely-color our choropleth maps by setting color to a non-numerical column, like the name of the winner of an election.
import plotly.express as px
df = px.data.election()
geojson = px.data.election_geojson()
fig = px.choropleth_map(df, geojson=geojson, color="winner",
locations="district", featureidkey="properties.district",
center={"lat": 45.5517, "lon": -73.7073},
map_style="carto-positron", zoom=9)
fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
fig.show()
import plotly.express as px
import geopandas as gpd
df = px.data.election()
geo_df = gpd.GeoDataFrame.from_features(
px.data.election_geojson()["features"]
).merge(df, on="district").set_index("district")
fig = px.choropleth_map(geo_df,
geojson=geo_df.geometry,
locations=geo_df.index,
color="Joly",
center={"lat": 45.5517, "lon": -73.7073},
map_style="open-street-map",
zoom=8.5)
fig.show()
If Plotly Express does not provide a good starting point, it is also possible to use the more generic go.Choroplethmap class from plotly.graph_objects.
from urllib.request import urlopen
import json
with urlopen('https://raw.githubusercontent.com/plotly/datasets/master/geojson-counties-fips.json') as response:
counties = json.load(response)
import pandas as pd
df = pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/fips-unemp-16.csv",
dtype={"fips": str})
import plotly.graph_objects as go
fig = go.Figure(go.Choroplethmap(geojson=counties, locations=df.fips, z=df.unemp,
colorscale="Viridis", zmin=0, zmax=12,
marker_opacity=0.5, marker_line_width=0))
fig.update_layout(map_style="carto-positron",
map_zoom=3, map_center = {"lat": 37.0902, "lon": -95.7129})
fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
fig.show()
Mapbox traces are deprecated and may be removed in a future version of Plotly.py.
The earlier examples using px.choropleth_map and go.Choroplethmap use Maplibre for rendering. These traces were introduced in Plotly.py 5.24 and are now the recommended way to create tile-based choropleth maps. There are also choropleth traces that use Mapbox: px.choropleth_mapbox and go.Choroplethmapbox
To plot on Mapbox maps with Plotly you may need a Mapbox account and a public Mapbox Access Token. See our Mapbox Map Layers documentation for more information.
Here's an exmaple of using the Mapbox Light base map, which requires a free token.
token = open(".mapbox_token").read() # you will need your own token
from urllib.request import urlopen
import json
with urlopen('https://raw.githubusercontent.com/plotly/datasets/master/geojson-counties-fips.json') as response:
counties = json.load(response)
import pandas as pd
df = pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/fips-unemp-16.csv",
dtype={"fips": str})
import plotly.graph_objects as go
fig = go.Figure(go.Choroplethmapbox(geojson=counties, locations=df.fips, z=df.unemp,
colorscale="Viridis", zmin=0, zmax=12, marker_line_width=0))
fig.update_layout(mapbox_style="light", mapbox_accesstoken=token,
mapbox_zoom=3, mapbox_center = {"lat": 37.0902, "lon": -95.7129})
fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
fig.show()
See function reference for px.choropleth_map or https://plotly.com/python/reference/choroplethmap/ for more information about the attributes available.
For Mapbox-based tile maps, see function reference for px.choropleth_mapbox or https://plotly.com/python/reference/choroplethmapbox/.
Dash is an open-source framework for building analytical applications, with no Javascript required, and it is tightly integrated with the Plotly graphing library.
Learn about how to install Dash at https://dash.plot.ly/installation.
Everywhere in this page that you see fig.show(), you can display the same figure in a Dash application by passing it to the figure argument of the Graph component from the built-in dash_core_components package like this:
import plotly.graph_objects as go # or plotly.express as px
fig = go.Figure() # or any Plotly Express function e.g. px.bar(...)
# fig.add_trace( ... )
# fig.update_layout( ... )
from dash import Dash, dcc, html
app = Dash()
app.layout = html.Div([
dcc.Graph(figure=fig)
])
app.run_server(debug=True, use_reloader=False) # Turn off reloader if inside Jupyter